Acute Cigarette Smoke–Induced Connective Tissue Breakdown Requires both Neutrophils and Macrophage Metalloelastase in Mice
Bibliographic record
Abstract
The cells/proteases responsible for the development of smoke-induced emphysema is an area of intense investigation. Mice with knockout of macrophage metalloelastase genes (MME(-/-)) do not develop emphysema after smoke exposure, but we also observed that neutrophils (PMN) in lavage appeared to be a requirement for acute connective tissue breakdown. In this study we exposed mice to cigarette smoke and examined lavage PMN, macrophages (MAC), desmosine (DES, a measure of elastin breakdown) and hydroxyproline (HP, a measure of collagen breakdown) 24 h afterwards. MME(+/+) mice exposed to smoke showed elevations in PMN, DES, and HP, but no elevations were seen in MME-deficient mice. Both PMN influx and increased levels of DES/HP could be restored by administering MAC from MME(+/+) mice to MME-deficient mice and then exposing them to smoke. RS113456, a metalloprotease inhibitor, also prevented PMN influx and connective tissue breakdown. Western blots against mouse alpha(1)-antitrypsin (alpha(1)AT) showed that alpha(1)AT was not protected in MME-deficient mice, nor by administration of RS113456. We conclude that, in mice, acute smoke-induced connective tissue breakdown, the precursor to emphysema, requires both PMN and MME, that PMN influx appears to be secondary to MAC activation, and that this process initially does not involve protection of alpha(1)AT from metalloprotease attack.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".